Direct additive, dominance and genetic parameters for ultrasound scan traits of a multi-breed population involving European, British, Sanga and Brahman breed types were estimated. A generalized ridge regression technique was used to eliminate high associations among some of the genetic effects in the model. Clear breed type effects were observed for all scanned traits. European breeds had negative and positive direct additive genetic effects for fat and eye muscle area, respectively in both heifers and bulls. British, Sanga and Brahman had positive direct additive effects for scan traits in heifers and bulls. Estimated heterosis of Brahman crosses were higher than non Brahman crosses for fat traits and ranged from 2% to 13%. The estimated heritabilities for rump fat, rib fat and eye muscle area of heifers were 0.36, 0.34 and 0.36 and for bulls 0.33, 0.23 and 0.39, respectively.

Single step genomic BLUP (SS-GBLUP) for BREEDPLAN beef cattle evaluations is currently being tested for implementation across a number of breeds. A genomic data pipeline has been developed to enable efficient analysis of the industry-recorded SNP genotypes for incorporation in SS-GBLUP analyses. Complex data collection, along with format and/or naming convention inconsistencies challenges efficient data processing. This pipeline includes quality control of variable formatted data, and imputation of genotypes, for building the genomic relationship matrix required for implementation into single step evaluation.

A study was conducted to quantify the separate and combined impacts on selection for economic merit of including residual feed intake (RFI) traits in beef cattle breeding objectives and of having records available. RFI is a trait of interest in numerous livestock species. It was defined here for young animals at pasture (RFI-P), in the feedlot (RFI-F), and in cows (RFI-C). Results showed selection response in total economic merit increased by up to 65% for breeding objectives where RFI-P, RFI-F, and RFI-C were all included. A large proportion of the benefit (more than 50%) came from being able to include RFI traits in the breeding objective, suggesting major benefits may be realised even where a suitable industry measure is not yet available. Residual feed intake should be considered in breeding objectives and selection where parameter estimates are available. Estimates of genetic variance are among those most needed for RFI-C, and are likely to need to be understood in cows that are approximately maintaining or even losing weight.

The genetics of reproduction is poorly understood because the heritabilities of traits currently recorded are low. To elucidate the genetics underlying reproduction in beef cattle, we performed a genome-wide association study using the bovine SNP50 chip in 2 tropically adapted beef cattle breeds, Brahman and Tropical Composite. Here we present the results for 3 female reproduction traits: 1) age at puberty, defined as age in days at first observed corpus luteum (CL) after frequent ovarian ultrasound scans (AGECL); 2) the postpartum anestrous interval, measured as the number of days from calving to first ovulation postpartum (first rebreeding interval, PPAI); and 3) the occurrence of the first postpartum ovulation before weaning in the first rebreeding period (PW), defined from PPAI. In addition, correlated traits such as BW, height, serum IGF1 concentration, condition score, and fatness were also examined. In the Brahman and Tropical Composite cattle, 169 [false positive rate (FPR) = 0.262] and 84 (FPR = 0.581) SNP, respectively, were significant (P < 0.001) for AGECL. In Brahman, 41% of these significant markers mapped to a single chromosomal region on BTA14. In Tropical Composites, 16% of these significant markers were located on BTA5. For PPAI, 66 (FPR = 0.67) and 113 (FPR = 0.432) SNP were significant (P < 0.001) in Brahman and Tropical Composite, respectively, whereas for PW, 68 (FPR = 0.64) and 113 (FPR = 0.432) SNP were significant (P < 0.01). In Tropical Composites, the largest concentration of PPAI markers were located on BTA5 [19% (PPAI) and 23% (PW)], and BTA16 [17% (PPAI) and 18% (PW)]. In Brahman cattle, the largest concentration of markers for postpartum anestrus was located on BTA3 (14% for PPAI and PW) and BTA14 (17% PPAI). Very few of the significant markers for female reproduction traits for the Brahman and Tropical Composite breeds were located in the same chromosomal regions. However, fatness and BW traits as well as serum IGF1 concentration were found to be associated with similar genome regions within and between breeds. Clusters of SNP associated with multiple traits were located on BTA14 in Brahman and BTA5 in Tropical Composites.

This study investigated the genetic relationships of blood hormones, scrotal size, body weight, condition score and flight time measured on young bulls to 12 months of age with key reproductive traits in Brahman and Tropical Composite breeds (n=4079). Heritability of the traits ranged from 0.17 to 0.72 indicating potential for genetic change in both populations. Genetic correlations with presence of sperm in the ejaculate at 12 months of age, percent normal sperm at 2 years old, and heifer age at puberty were moderate, in some cases up to 0.61, indicating a potential to improve the efficiency of selection of breeding replacements.

Heritabilities and genetic correlations for male and female reproduction traits were estimated for Brahman cattle raised in northern Australia. The traits included the female reproduction traits of days to calving (DC), age at puberty (AP) and lactation anoestrous interval (LAI). Days to calving using repeat records (DCr) was further considered as separate DC traits for first (DC1) and second parity (DC2) records, as well as a simple binary trait for calving rate (CR). Male reproduction traits included scrotal circumference (SC) and percent normal sperm (PNS) measured in young bulls. The heritability estimates for DCr, CR, DC1, DC2, AP, LAI, SC and PNS, were 0.09, 0.10, 0.09, 0.15, 0.47, 0.40, 0.44 and 0.15, respectively. Genetic correlations between DC1 and AP, LAI, SC and PNS were 0.62, 0.52, -0.32 and -0.66, respectively. For DC2, the genetic correlation with DC1, AP, LAI, SC and PNS were 0.46, 0.56, 1.0, -0.29 and -0.71, respectively. The study has shown that the various reproduction traits were heritable. The 0.46 genetic correlation between DC1 and DC2 suggests they should be considered as separate traits in genetic evaluation and this would allow fitting different genetic correlations with important component traits. Improvement of the genetic evaluation will increase accuracies of female reproduction EBVs and allow more genetic progress in tropical beef breeds in northern Australia.

Genomic selection is rapidly changing dairy breeding but to date it has had little impact on beef cattle breeding. The challenge for beef is to increase the accuracy of genomic predictions, particularly for those traits that cannot be measured on young animals. Accuracies of genomic predictions in beef cattle are low, primarily due to the relatively low number of animals with genotypes and phenotypes that have been used in gene discovery. To improve this will require the collection of genotypes and phenotypes on many more animals. Several key industry initiatives have commenced in Australia aimed at addressing this issue. Also, unlike dairy, the beef industry includes several major breeds and this will likely require the use of very dense SNP chips to enable accurate genomic prediction equations that are predictive across breeds. In Australia genotyping has been performed on all major breeds and research is underway to ascertain the effectiveness of a high density SNP chip (800K) to increase the accuracy of prediction. However, at this stage it is apparent, even in dairy breeding, that genomic information is best combined with traditional pedigree and performance data to generate genomically-enhanced EBVs, thus allowing greater rates of genetic gain through increased accuracies and reduced generation intervals. Several methods exist for combining the two sources of data into current genetic evaluation systems; however challenges exist for the beef industry to implement these effectively. Over time, as the accuracy of genomic selection improves for beef cattle breeding, changes are likely to be needed to the structure of the breeding sector to allow effective use of genomic information for the benefit of the industry.

Beef CRC research showed that a subjective score of teat size (small (1) to large (5)) was heritable in tropically adapted Brahman (BRAH) and Tropical Composite (TCOMP) cows, and that higher teat scores (bottle teats) were genetically associated with higher calf losses from birth to weaning. Teat traits are only expressed in females, and the research showed that they tended to display more variation in later life; making them ideal candidates for genomic selection. Front and rear teat scores (TSF and TSB respectively) were recorded in cows at calving through up to 6 matings. From these, a trait was created which described a cows maximum lifetime teat score (TSM), as well as a binary trait which distinguished cows that received a teat score of 4 or 5 at any time through their lives (1) from those which did not (0) (MSB). Results confirmed the heritability of TSF and TSB (h² = 0.30 to 0.40), and variation in both TSM and MSB was also shown to have a genetic basis (h² = 0.49 and 0.46 respectively for BRAH, and 0.29 and 0.22 for TCOMP). Genome wide association analyses identified large numbers of significant SNPs but did not suggest a likelihood of identifying a small number of SNPs of large effect. It is unlikely therefore, that a simple diagnostic test (based a small number of SNPs) could be developed for the traits. Conventional genomic selection, however, is likely to present opportunities to improving teat traits by selection in tropically adapted beef genotypes, with accuracies of genomic prediction of 0.23 to 0.35 for TSM and MSB across both genotypes.

Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull. Increasing the accuracy of these traits is where genomic selection can offer substantial improvements in current rates of genetic gain for beef. The immediate challenge for beef is to increase the genetic variation explained by the genomic predictions for those traits of high economic value that have low accuracies at the time of selection. Currently, the accuracies of genomic predictions are low in beef, compared with those in dairy cattle. This is likely to be due to the relatively low number of animals with genotypes and phenotypes that have been used in developing genomic prediction equations. Improving the accuracy of genomic predictions will require the collection of genotypes and phenotypes on many more animals, with even greater numbers needed for lowly heritable traits, such as female reproduction and other fitness traits. Further challenges exist in beef to have genomic predictions for the large number of important breeds and also for multi-breed populations. Results suggest that single-nucleotide polymorphism (SNP) chips that are denser than 50 000 SNPs in the current use will be required to achieve this goal. For genomic selection to contribute to genetic progress, the information needs to be correctly combined with traditional pedigree and performance data. Several methods have emerged for combining the two sources of data into current genetic evaluation systems; however, challenges exist for the beef industry to implement these effectively. Changes will also be needed to the structure of the breeding sector to allow optimal use of genomic information for the benefit of the industry. Genomic information will need to be cost effective and a major driver of this will be increasing the accuracy of the predictions, which requires the collection of much more phenotypic data than are currently available.

Multi-trait single step genetic evaluation is increasingly facing the situation of having more individuals with genotypes than an individuals’ genotype has markers. This leads to an algebraically impossible inversion of the genomic relationship matrix (G). Recent derivations in single step equations called SS-T-BLUP have provided an elegant way to circumvent the inversion of the G and therefore accommodate the described situation. In this paper we examine the applicability of the SST-BLUP model to the multi-trait Australian Angus BREEDPLAN genetic evaluation and compare the results to applying two different ways of using G in a single step model. Results clearly show that SS-T-BLUP outperforms other single step formulations and allows users to avoid approximating the inverse of G.